A further enhanced machine-learning (adaptive fuzzy) approach, to infer transcriptional interactions (Tis), which incorporates DNA sequence, ChIP-chip and microarray data. AdaFuzzy proposes a robust position weight matrix and a feature vector. Furthermore, potential TF binding sites in upstream sequences of a specific target gene are identified by an adaptive neuro-fuzzy inference system (ANFIS) using sequence data. ChIP-chip data confirms that TIs do indeed occur under specific experimental conditions. In addition, microarray data is used to classify predicted TIs into activator-target or repressor-target relations via a weighted regression. After potential TIs are identified, AdaFuzzy also classifies their types of promoter architectures to provide insights into the organization of transcriptional regulatory interactions.